본문으로 바로가기 메뉴 바로가기
Loading...

전체메뉴

연구성과

마이크로바이옴의 올바른 해석을 통한 건강한 세상 만들기, 이지놈이 시작합니다.

연구성과

게시물 검색
Expression and Purification of Extracellular Solute-Binding Protein (ESBP) in Escherichia coli, the Extracellular Protein Derived from Bifidobacterium longum KACC 91563썸네일
Microbiomics
Food Sci. Anim. Resour. 31 Aug 2019

Bifidobacterium longum KACC 91563 secretes family 5 extracellular solute-binding protein via extracellular vesicle. In our previous work, it was demonstrated that the protein effectively alleviated food allergy symptoms via mast cell specific apoptosis, and it has revealed a therapeutic potential of this protein in allergy treatment. In the present study, we cloned the gene encoding extracellular solute-binding protein of the strain into the histidine-tagged pET-28a(+) vector and transformed the resulting plasmid into the Escherichia coli strain BL21 (DE3). The histidine-tagged extracellular solute-binding protein expressed in the transformed cells was purified using Ni-NTA affinity column. To enhance the efficiency of the protein purification, three parameters were optimized; the host bacterial strain, the culturing and induction temperature, and the purification protocol. After the process, two liters of transformed culture produced 7.15 mg of the recombinant proteins. This is the first study describing the production of extracellular solute-binding protein of probiotic bacteria. Establishment of large-scale production strategy for the protein will further contribute to the development of functional foods and potential alternative treatments for allergies.

Abstract +
Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data썸네일
Microbiomics
Scientific Reports 15 Jul 2019

Diseases prediction has been performed by machine learning approaches with various biological data. One of the representative data is the gut microbial community, which interacts with the host’s immune system. The abundance of a few microorganisms has been used as markers to predict diverse diseases. In this study, we hypothesized that multi-classification using machine learning approach could distinguish the gut microbiome from following six diseases: multiple sclerosis, juvenile idiopathic arthritis, myalgic encephalomyelitis/chronic fatigue syndrome, acquired immune deficiency syndrome, stroke and colorectal cancer. We used the abundance of microorganisms at five taxonomy levels as features in 696 samples collected from different studies to establish the best prediction model. We built classification models based on four multi-class classifiers and two feature selection methods including a forward selection and a backward elimination. As a result, we found that the performance of classification is improved as we use the lower taxonomy levels of features; the highest performance was observed at the genus level. Among four classifiers, LogitBoost–based prediction model outperformed other classifiers. Also, we suggested the optimal feature subsets at the genus-level obtained by backward elimination. We believe the selected feature subsets could be used as markers to distinguish various diseases simultaneously. The finding in this study suggests the potential use of selected features for the diagnosis of several diseases.

Abstract +

전체메뉴